Associates of an Elevated Natriuretic Peptide Level in Stable Heart Failure Patients: Implications for Targeted Management
Bibliographic record
Abstract
BACKGROUND: Persistently elevated natriuretic peptide (NP) levels in heart failure (HF) patients are associated with impaired prognosis. Recent work suggests that NP-guided therapy can improve outcome, but the mechanisms behind an elevated BNP remain unclear. Among the potential stimuli for NP in clinically stable patients are persistent occult fluid overload, wall stress, inflammation, fibrosis, and ischemia. The purpose of this study was to identify associates of B-type natriuretic peptide (BNP) in a stable HF population. METHODS: In a prospective observational study of 179 stable HF patients, the association between BNP and markers of collagen metabolism, inflammation, and Doppler-echocardiographic parameters including left ventricular ejection fraction (LVEF), left atrial volume index (LAVI), and E/e prime (E/e') was measured. RESULTS: Univariable associates of elevated BNP were age, LVEF, LAVI, E/e', creatinine, and markers of collagen turnover. In a multiple linear regression model, age, creatinine, and LVEF remained significant associates of BNP. E/e' and markers of collagen turnover had a persistent impact on BNP independent of these covariates. CONCLUSION: Multiple variables are associated with persistently elevated BNP levels in stable HF patients. Clarification of the relative importance of NP stimuli may help refine NP-guided therapy, potentially improving outcome for this at-risk population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".